Implementation-wise, we create and evaluate a single-task (unigram only) baseline model, following the CNN + Bidirectional LSTM paradigm [1]}, while proposing several computational efficient modifications.
Concerning the proposed multi-task approach, using the single-task model as backbone, each level of decomposition (unigram, bigram, e.t.c.) corresponds to a different model branch which has its own CTC loss.
The architectural choices with respect to the task-specific model branches lead to two alternatives, inspired by [2]}: Hierarchical Multi-task and Block Multi-task Architectures.
Assuming a multi-task learning methodology, we aim to implicitly impose context-rich language information to intermediate generated features, previous to the final task related output.
To this end, we evaluate the proposed approach by performing a conventional decoding process on the unigram output.
This way, we assess how multiple decompositions can enhance the internal representations of the model, while maintaining the inference time of the single-task model and preserving the simplicity of the typical decoding process.
